Self-calibrating glucose monitor

ABSTRACT

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

TECHNICAL FIELD

This disclosure is related to a medical system for evaluating a glucoselevel of a patient.

BACKGROUND

Physiological characteristic sensors may be use in a variety ofspecialized applications. For example, implantable sensors may be usedin glucose monitoring systems to facilitate treatment of diabetes, suchas monitoring glucose levels over time for adjusting a treatment regimenthat includes regular administration of insulin to a patient. Naturallyproduced insulin may not control the glucose level in the bloodstream ofa diabetes patient due to insufficient production of insulin and/or dueto insulin resistance. To control the glucose level, a patient's therapyroutine may include dosages of basal insulin and bolus insulin based ona monitored glucose level.

SUMMARY

Examples of a medical system disclosed here include processing circuitryconfigured to receive a cardiac signal from sensing circuitry and aglucose signal from a glucose sensor. The cardiac signal is indicativeof a cardiac characteristic of a patient. The glucose signal isindicative of a glucose level of the patient. The processing circuitryis configured to formulate a training data set including one or moretraining input vectors using the cardiac signal and one or more trainingoutput vectors using the glucose signal. The processing circuitry isconfigured to train a machine learning algorithm using the formulatedtraining data set. Using the trained machine learning algorithm, theprocessing circuitry is configured to determine a representative glucoselevel using a current cardiac signal from the patient. The processingcircuitry may be configured to deactivate the glucose sensor once themachine learning algorithm is sufficiently trained to provide therepresentative glucose level, potentially extending a life of theglucose sensor.

In an example, medical system comprises: a glucose sensor configured todetermine a glucose level in a patient; and processing circuitryoperably coupled to the glucose sensor, the processing circuitryconfigured to: receive a glucose signal indicative of the glucose levelfrom the glucose sensor, receive a cardiac signal indicative of acardiac characteristic of the patient, associate the glucose signal withthe cardiac signal, formulate one or more training data sets including atraining input vector and a training output vector, wherein the traininginput vector is representative of the cardiac signal and the trainingoutput vector is representative of the glucose signal associated withthe cardiac signal, train a machine learning algorithm using the one ormore training data sets, and determine a representative glucose levelusing the trained machine learning algorithm and a current cardiacsignal, wherein the current cardiac signal is indicative of a currentcardiac characteristic of the heart of the patient.

In an example, a medical system comprises: a glucose sensor configuredto determine a glucose level in a patient; sensing circuitry configuredto sense a cardiac characteristic of a heart of the patient; processingcircuitry operably coupled to the glucose sensor and the sensingcircuitry, the processing circuitry configured to: receive an cardiacsignal indicative of the cardiac characteristic from the sensingcircuitry, identify a cardiac marker using the cardiac signal, receive aglucose signal indicative of the glucose level from the glucose sensor,associate the cardiac marker with the glucose signal, formulate one ormore training data sets including a training input vector and a trainingoutput vector, wherein the training input vector is representative ofthe cardiac signal and the training output vector is representative ofthe glucose signal associated with the glucose signal, train a machinelearning algorithm using the one or more training data sets, anddetermine a representative glucose level using the trained machinelearning algorithm and a current cardiac signal received from thesensing circuitry, wherein the current cardiac signal is indicative of acurrent cardiac characteristic of the heart of the patient; and ahousing mechanically supporting the glucose sensor and the processingcircuitry,

In an example, a method comprises: receiving, using processingcircuitry, an ECG signal indicative of an electrocardiogram of a heartof a patient from sensing circuitry configured to sense theelectrocardiogram, receiving, using the processing circuitry, a glucosesignal indicative of a glucose level of the patient from a glucosesensor configured to determine the glucose level in the patient,associating, using the processing circuitry, the ECG signal with theglucose level, formulating, using the processing circuitry, one or moretraining data sets including a training input vector and a trainingoutput vector, wherein the training input vector is representative ofthe ECG signal and the training output vector is representative of theglucose signal associated with the ECG signal, training, using theprocessing circuitry, a machine learning algorithm using the one or moretraining data sets, and determining, using the processing circuitry, arepresentative glucose level using the trained machine learningalgorithm and a current cardiac signal, wherein the current cardiacsignal is indicative of a current cardiac characteristic of the heart ofthe patient.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example medical systemincluding a glucose sensor.

FIG. 2 is a example flow diagram for processing circuitry of a medicalsystem.

FIG. 3 is a conceptual diagram of a medical system including sensingcircuitry.

FIG. 4 is a conceptual diagram of an electrode of a glucose sensor.

FIG. 5 is a conceptual diagram of a plurality of glucose sensors.

FIG. 6 is a flow diagram for processing circuitry of an example medicalsystem.

FIG. 7 is a conceptual diagram of a medical system including a network.

FIG. 8 illustrates an example technique for using a medical system todetermine a representative glucose level.

DETAILED DESCRIPTION

This disclosure describes a medical system including processingcircuitry configured to train a machine learning algorithm using acardiac characteristic of a heart and a glucose level of a patient. Thecardiac characteristic may be representative of the cardiac electricalactivity of the heart of the patient, such as an electrocardiogram(ECG), an electrogram (EGM), or another measure. The medical system mayinclude sensing circuitry to provide the cardiac characteristic of thepatient and a glucose sensor to provide the glucose level of thepatient. The processing circuitry is configured to associate someportion of the cardiac characteristic with a glucose level received andformulate a training data set to train the machine learning algorithm.Subsequent to training, the medical system is configured to determine arepresentative glucose level of the patient based on a current cardiaccharacteristic reflecting current cardiac activity of the patient. Themedical system may thus substantially personalize the machine learningalgorithm to the cardiac characteristics of a patient by formulating thetraining set and training the machine learning algorithm usingphysiological indications individual to the patient. In examples, theprocessing circuitry is configured to perform a calibration check of thetrained machine learning algorithm by comparing the representativeglucose level indicated to a current glucose level of the patient, andfurther training or re-training the machine learning algorithm based onthe comparison.

The medical system may include a wearable, implantable, and/or portabledevice including a housing configured to contact a body (e.g., a torso)of the patient. The housing may mechanically support one or processorsconfigured to receive a cardiac signal indicative of the cardiaccharacteristic of the patient and a glucose signal indicative of theglucose level of the patient. The processing circuitry is configured toformulate one or more training data sets using the cardiac signal andthe glucose signal. In examples, the one or more training data setsinclude a plurality of training input vectors representative of thecardiac signal and a plurality of training output vectors representativeof the glucose signal, with each training input vector associated with acorresponding training output vector. The one or more processors areconfigured to train the machine learning algorithm with the trainingdata set such that the processing circuitry may subsequently receive acurrent cardiac signal indicative of cardiac activity of the patient anddetermine a representative glucose level using the current cardiacsignal. In examples, the one or more processors comprise a memory andone or more processing circuits configured to enact the machine learningalgorithm.

In examples, the housing includes a glucose sensor configured to providethe glucose signal indicative of the glucose level to the one or moreprocessors. The processing circuitry may be configured to cause theglucose sensor to activate to provide the glucose signals (e.g., whileformulating the training data set), and may be configured to cause theglucose sensor to deactivate when the glucose signals are no longeractively required (e.g., once the training data set is formulated,and/or once the machine learning algorithm is trained). Thus, themedical system may be configured to substantially reduce and/or minimizethe activated time of an on-board glucose sensor, potentially extendingthe life of the glucose sensor and decreasing the need for periodicreplacement.

In some examples, the housing of the wearable, implantable, and/orportable device mechanically supports a plurality of individual glucosesensors. The processing circuitry may be configured to selectivelyexpose a first individual glucose sensor in the plurality to, forexample, the interstitial fluid or blood of a patient to generate theglucose signal when required (e.g., while formulating training dataand/or conducting a calibration check). The processing circuitry may beconfigured to deactivate the first individual glucose sensor andactivate a second individual glucose sensor in the plurality when areplacement criteria for the first individual glucose sensor is met(e.g., when the processing circuitry detects a degraded signal,following the elapse of a chronological period of use, and/or foranother reason).

The processing circuitry may be configured to periodically perform acalibration check of the trained machine learning algorithm bydetermining a current glucose level of the patient using the glucosesensor and comparing the current glucose level to a representativeglucose level determined for the patient by the trained machine learningalgorithm. Based on the comparison, the processing circuitry may beconfigured to formulate additional training data and further train orre-train the machine learning algorithm using the additional trainingdata. The processing circuitry may thus be configured to substantiallyadjust the response of the machine learning algorithm based onindividual changes of the physiological parameters of the patient.

In examples, the processing circuitry is configured to identify acardiac marker using the cardiac signal and associate the cardiac markerwith a glucose signal provided by the glucose sensor. The cardiac markermay be is at least one of, for example, a heart rate variability (HRV),a QT internal variability (QTV), a corrected QT interval (QTc), an STinterval, an ST elevation, a T wave amplitude, a T-peak to T-endinterval, a T slope, a T-wave area, a T-wave asymmetry, an R-waveamplitude, a T-wave amplitude, and/or another identifiablecharacteristic of the cardiac signal of the patient. The processingcircuitry may be configured to identify a value of the cardiac markerwithin an segment of the cardiac signal received over a time interval(e.g., an ECG segment and/or an EGM segment), and may be configured toassociate a glucose signal received from the glucose sensor with a valueof the cardiac marker. The processing circuitry may formulate a traininginput vector representative of the value of the cardiac marker and acorresponding training output vector representative of the receivedglucose signal. In examples, the processing circuitry identifies a valueof the cardiac marker and an associated glucose signal for a pluralityof segments of the cardiac signal, and formulates a training inputvector and corresponding training output vector for each segment.

The medical system may include sensing circuitry configured to sense thecardiac characteristic of the patient and generate the cardiac signaland/or cardiac marker. The sensing circuitry may be configured tocommunicate the cardiac signal and/or cardiac marker to the processingcircuitry. For example, the medical system may be at least partiallyincorporated into a medical device including a housing whichmechanically supports the processing circuitry and the sensingcircuitry. In some examples, the medical device includes one or moreelectrodes configured to contact the patient and sense the cardiaccharacteristic of the heart. The housing of the medical device maymechanically support the electrodes. In other examples, the processingcircuitry may be configured to receive the cardiac signal and/or cardiacmarker from another system or device configured to detect andcommunicate the cardiac signal and/or cardiac marker. For example, theprocessing circuitry may be configured to receive the cardiac signaland/or cardiac marker from an implantable and/or wearable cardiographysystem configured to sense a cardiac signal and/or cardiac marker of thepatient and communicate the cardiac signal and/or cardiac marker to theprocessing circuitry.

In some examples, the medical system includes an infusion deviceconfigured to provide a therapeutic fluid (e.g., insulin) to thepatient. For example, the medical system may include a fluid deliverycannula configured to deliver a fluid (e.g., insulin) to the patient.The medical system may include a fluid pump (e.g., an insulin pump)configured for fluid communication with the fluid delivery cannula. Thefluid pump may be configured to deliver the fluid to the fluid deliverycannula from a fluid reservoir of the medical system. The fluidreservoir may be, for example, a volume defined by a detachable fluidcartridge configured to mechanically engage a housing of the medicalsystem and to establish a fluidic connection with the fluid pump. Inexamples, the processing circuitry is configured to control an operationof the fluid pump. For example, the processing circuitry may beconfigured to cause the fluid pump to commence, continue, and/or ceasecausing transportation of fluid from the fluid reservoir through thefluid delivery cannula based on the representative glucose leveldetermined by the trained machine learning algorithm. In some examples,when the medical system is at least partially incorporated into amedical device including a housing, the housing may mechanically supportthe fluid delivery cannula, the fluid pump, the fluid reservoir, and/orother components of an infusion device.

The medical system may include a user interface for presentinginformation to and receiving input from the patient. For example, theuser interface may be configured to generate a visual display viewableby the patient and providing information such as a representativeglucose level determined at a discrete chronological time or over a timeinterval, a graph of representative glucose levels, a period of usesince the most recent training and/or calibration check, a status of anindividual glucose sensor, an accuracy or alert based on a calibrationcheck, and/or other information arising through operation of the medicalsystem. The user interface may be configured to cause the processingcircuitry to perform certain functions based on an input from thepatient. For example, user interface may cause the processing circuitryto formulate a training data set, train the machine learning algorithm,perform a calibration check of the trained machine learning algorithm,cause a glucose sensor to generate a current glucose signal, and/orother functions.

In examples, the medical system may be implemented using one or morecomputer programs implemented on programmable computers, such ascomputers that include, for example, processing capabilities, datastorage (e.g., volatile or nonvolatile memory and/or storage elements),input devices, and output devices. Program code and/or logic describedherein may be applied to input data to perform the functionalitydescribed herein and generate desired output information. The programsmay be stored on any suitable device, e.g., a storage media, readable bya general or special purpose program running on a computer system (e.g.,including processing apparatus) and configuring the computer system toperform functions described herein. Computer-implemented instructions,data structures, screen displays, and other data under aspects of thetechnology may be stored or distributed on computer-readable storagemedia, including magnetically or optically readable computer disks, asmicrocode on semiconductor memory, nanotechnology memory, organic oroptical memory, or other portable and/or non-transitory data storagemedia. In some embodiments, aspects of the technology may be distributedover the Internet or over other networks (e.g., a Bluetooth network) ona propagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave) over a period of time, or may be provided on anyanalog or digital network (packet switched, circuit switched, or otherscheme).

FIG. 1 is a conceptual diagram of an example medical system 100configured to determine a representative glucose level based on acardiac signal indicative of the cardiac activity of a heart 102 of apatient 104. Medical system 100 includes processing circuitry 106mechanically supported within a housing 108 and a glucose sensor 110supported within a sensor housing 112. In examples, housing 108mechanically supports both processing circuitry 106 and glucose sensor110.

Processing circuitry 106 is configured to receive a cardiac signal(e.g., an ECG or EGM signal) indicative of a cardiac characteristic ofheart 102 of patient 104. In examples, processing circuitry 106 isconfigured to receive the cardiac signal from sensing circuitry 116 ofcardiography system 114 via a communication link 115. Cardiographysystem 114 may be configured to sense and communicate the cardiac signalfrom a position outside of heart 102 and/or using additional deviceslocated in closer proximity to heart 102, such as sensor 118. Sensingcircuitry 116 may be configured to sense the cardiac characteristic ofheart 102 and communicate the cardiac signal to processing circuitry 106(e.g., via communication link 115). Processing circuitry 106 is furtherconfigured to receive a glucose signal indicative of a glucose level ofpatient 104 from glucose sensor 110. For example, processing circuitry106 may be configured to receive the glucose signal via a communicationlink 120.

Processing circuitry 106 is configured to use a machine learningalgorithm to indicate a representative glucose level indicative of aglucose level of patient 104 using a current cardiac signal. Processingcircuitry 106 configured to train the machine learning algorithm toindicate the representative glucose level using the current cardiacsignal. Processing circuitry 106 is configured to formulate one or moretraining data sets using the cardiac signal and glucose signal generatedfrom the physiological characteristics of patient 104, then train themachine learning algorithm using the training data set. The trainedmachine learning algorithm is thus trained using training datapersonalized to patient 104, rather than training data aggregated from aplurality of patients. Such personalized training data may improve theaccuracy of the representative glucose level obtained when the trainedmachine learning algorithm receives a current cardiac signal and/orcardiac marker indicative of the cardiac activity of patient 104 andmaps the cardiac signal and/or cardiac marker to an output glucose levelto obtain the representative glucose level.

Processing circuitry 106 is thereby configured to determine arepresentative glucose level of patient 104 using a current cardiacsignal and/or cardiac marker of patient 104 without the necessity for anattendant current glucose signal from glucose sensor 110. Thedetermination of the representative glucose level of patient 104 withoutthe necessity for the attendant current glucose signal may extend theoperational life of glucose sensor 110. In examples, processingcircuitry 106 is configured to cause glucose sensor 110 to activateand/or deactivate based on a command signal generated by processingcircuitry 106. For example, while formulating the training data set forthe machine learning algorithm, processing circuitry might issue acommand signal causing glucose sensor 110 to activate (e.g., establishan activated configuration) and commence sensing a glucose level withinthe blood or interstitial fluid of patient 104. Glucose sensor 110 maybe configured to periodically and/or substantially continuouslycommunicate a glucose signal indicative of the glucose level toprocessing circuitry 106 while activated. Processing circuitry 106 mayutilize the glucose signal received from glucose sensor 110 inconjunction with a cardiac signal received from cardiography system 114(e.g., sensing circuitry 116) to formulate a training data set includinga plurality of training input vectors and associated training outputvectors. When a sufficient training data set is obtained, processingcircuitry 106 may then issue a command causing glucose sensor 110 todeactivate (e.g., establish a deactivated configuration) andsubstantially cease generating the glucose signal, potentially extendingthe life of glucose sensor 110. Glucose sensor 110 may be configured togenerate the glucose signal indicative of the glucose level in anymanner, including sensing of electrochemical potentials, near-infra-redspectroscopy, impedance spectroscopy, Raman spectroscopy, tomography,photoacoustics, and/or other methods. A variety of glucose sensingmethods may be employed, such as methods based on monitoring the opticalproperties of intrinsically fluorescent or labeled enzymes, theirco-enzymes and co-substrates, the measurement of the products ofenzymatic oxidation of glucose by glucose oxidase, the use of syntheticboronic acids, the use of Concanavalin A, the application of otherglucose-binding proteins, and/or other methods.

For example, glucose sensor 110 may include an electrode (e.g., anelectrooxidizing anode) configured to cause an oxidation of glucoseusing a catalyst included in glucose sensor 110, such as a glucoseoxidase enzyme. Glucose sensor 110 may include an electrode (e.g., anelectrooxidizing anode) configured to receive electrical power from abattery within medical system 100 to cause the oxidation. Processingcircuitry 106 may be configured to activate glucose sensor 110 bycausing the electrode to receive the electrical power. Processingcircuitry 106 may be configured to deactivate glucose sensor 110 toprevent and/or minimize the electrical power received by the electrode.Deactivating glucose sensor 110 to reduce and/or substantially preventthe oxidation of glucose may extend the life of the electrode byreducing degradation of the included catalyst, or other portions ofglucose sensor 110. For example, processing circuitry 106 may beconfigured to activate glucose sensor 110 when one or more glucosesignals are required to formulate the training data set and/or perform acalibration check of the trained machine learning algorithm, and may beconfigured to deactivate glucose sensor 110 when processing circuitry106 utilizes the trained machine learning algorithm and a currentcardiac signal from cardiography system 114 (e.g., sensing circuitry116) to determine a representative glucose level. Such selectiveactivation and deactivation may extend the useful life of glucose sensor110.

Processing circuitry 106 is configured to initially train the machinelearning algorithm using the cardiac signal (e.g., received fromcardiography system 114) and the glucose signal received from glucosesensor 110. To train the machine learning algorithm, processingcircuitry 106 may be configured to formulate a training data set usingthe cardiac signal and the glucose signal indicative of thephysiological characteristics of patient 104. In examples, the trainingdata set formulated includes a plurality of training input vectorsrepresentative of the cardiac signal sensed for patient 104 and aplurality of training output vectors representative of the glucosesignal sensed for patient 104, with each training input vectorassociated with a corresponding training output vector. The plurality oftraining input vectors and the plurality of training output vectors arethus personalized to the physiology of patient 104. Processing circuitry106 is configured to train the machine learning algorithm using thepersonalized training data set, such that the trained machine learningalgorithm is trained to output a representative glucose level forpatient 104 when provided with an input vector representative of acurrent cardiac signal sensed for patient 104 (e.g., sensed bycardiography system 114).

FIG. 2 illustrates a flow diagram of an example technique which may beutilized by processing circuitry 106 to determine a representativeglucose level of patient 104 based on a cardiac signal indicative of aphysiological characteristic of heart 102. Processing circuitry 106 mayutilize all or any portion of the technique illustrated by FIG. 2. Asillustrated in FIG. 2, processing circuitry 106 receives a cardiacsignal indicative of a cardiac characteristic of heart 102 fromcardiography system 114 (e.g., sensing circuitry 116) (152). Processingcircuitry 106 may activate glucose sensor 110 (154), and receive aglucose signal indicative of a glucose level of patient 104 from glucosesensor 110 (156). Processing circuitry 106 may deactivate glucose sensor110 (158) once the glucose signals are no longer actively required(e.g., once the training data set is formulated) in order to, forexample, extend an operational life of glucose sensor 110 and/or anothercomponent of medical system 100.

Processing circuitry 106 utilizes the cardiac signal and the glucosesignal received to formulate a training data set (160). In examples, thetraining data set includes a plurality of training input vectorsindicative of the cardiac signal from cardiography system 114 (e.g.,sensing circuitry 116), with each training input vector associated witha training output vector indicative of the glucose signal from glucosesensor 110. In examples, processing circuitry 106 is configured toformulate a given training input vector comprising the training data setby receiving a cardiac signal from cardiography system 114 and dividingthe cardiac signal into a segment, where the cardiac segment is thecardiac signal received over a time interval. In examples, cardiographysystem 114 may be configured to divide the cardiac signal into a segmentand provide the segment to processing circuitry 106. Processingcircuitry 106 may be configured to identify an cardiac marker indicativeof and/or derived from the cardiac signal and/or cardiac segment. Thecardiac marker may be one or more identifiable physiologicalcharacteristics of heart 102 indicated by the cardiac signal of heart102. Processing circuitry 106 may formulate the given input vector bydefining one or more elements of the given input vector, where the oneor more elements are indicative of the cardiac signal, the cardiacsegment, and/or the cardiac marker identified over the time interval.

Processing circuitry 106 may be configured to formulate a trainingoutput vector associated with the given input vector by receiving and/orsampling a glucose signal from glucose sensor 110. Processing circuitry106 may formulate the associated training output vector by defining oneor more elements of the associated training output vector, where the oneor more elements are indicative of the received and/or sampled glucosesignal. In some examples, processing circuitry 106 is configured toreceive and/or sample the glucose signal utilized to formulate theassociated output vector substantially within the time interval of thecardiac segment utilized to formulate the given input vector. In someexamples, processing circuitry 106 is configured to receive and/orsample the glucose signal utilized to formulate the associated trainingoutput vector outside of the time interval of the cardiac segmentutilized to formulate the given input vector. For example, processingcircuitry 106 may be configured to receive and/or sample the glucosesignal utilized to formulate an associated training output vector afterthe time interval of the cardiac segment utilized to formulate the giveninput vector has elapsed, in order to account for, for example, a timelag in glucose transport when sensing glucose level in interstitialfluid of patient 104, and/or a time lag expected due to physiologicaland/or environmental parameters monitored by medical system 100, such asheart rate, blood pressure, posture, time of day or night, respirationrate, manual inputs relating to diet, and/or other physiological and/orenvironmental parameters.

Processing circuitry 106 may be configured to group the given traininginput vector and the associated training output vector into a data pair.In examples, processing circuitry 106 formulates a plurality of traininginput vectors indicative of a cardiac signal, a cardiac segment, and/ora cardiac marker received from cardiography system 114 for patient 104and associates a training output vector indicative of a glucose signalreceived from glucose sensor 110 for each training input vector.Processing circuitry 106 may group each training input vector andassociated training output vector in a data pair, such that processingcircuitry 106 formulates a plurality of data pairs. Processing circuitry106 may define a training data set using the plurality of data pairs.Thus, processing circuitry 106 may be configured to define the trainingdata set using data pairs substantially personalized to the physiologyof patient 104.

Processing circuitry 106 uses the training data set formulated to traina machine learning algorithm (162). Processing circuitry 106 may includeone or more processing circuits configured to implement the machinelearning algorithm, such as a neural network, a deep learning system, oranother type of machine learning system. In examples, processingcircuitry 106 is configured to implement the machine learning algorithmusing one or more neural network systems, deep learning systems, orother types of supervised or unsupervised machine learning systems. Forexample, the machine learning algorithm may be implemented by afeedforward neural network, such as a convolutional neural network, aradial basis function neural network, a recurrent neural network, amodular or associative neural network. Processing circuitry 106 trainsthe machine learning algorithm using the training data set personalizedto patient 104. In some examples, the machine learning algorithm may bepre-trained to some degree using data indicative of physiologicalcharacteristics gathered from a cohort of patients, and processingcircuitry 106 may be configured to further train the machine learningalgorithm using the training data set personalized to patient 104.

In examples, a neural network utilized by processing circuitry 106includes a plurality of artificial neurons. The artificial neurons maybe present within one or more layers of the neural network. For example,the artificial neurons may present within an input layer of the neuralnetwork, an output layer of the neural network, and one or more hiddenlayers between the input layer and the output layer. The input layer mayinclude one or more input artificial neurons. The output layer mayinclude one or more output artificial neurons. The artificial neuronsmay be configured to receive a signal at an input of the artificialneuron and process the signal at an output of the artificial neuron(e.g., process the signal using a parameter of the artificial neuron).The artificial neuron may include a plurality of inputs and a pluralityof outputs. The artificial neuron may be configured to receive the inputfrom the output of a separate artificial neuron, and may be configuredto pass the processed signal from its output to the input of anotherartificial neuron. The processing of the signal conducted by theartificial neuron may be adjusted by the artificial neuron as trainingof the machine learning algorithm proceeds.

Processing circuitry 106 is configured to train the machine learningalgorithm using the training data set formulated. In examples,processing circuitry 106 is configured to provide one or more elementsof a training input vector of the formulated training data set to theinputs of one or more input artificial neurons. The machine learningalgorithm may be configured to provide an resulting output vector at theoutputs of one or more output artificial neurons subsequent toprocessing circuitry 106 providing the training input vector to theinputs. The processing circuitry may be configured to provide one ormore elements of the training output vector associated with the traininginput vector as a desired output. In examples, the machine learningalgorithm is configured to determine an error between the resultingoutput vector produced using the training input vector and the desiredoutput of the associated training output vector. The machine learningalgorithm may be configured to adjust the processing employed by anartificial neuron (e.g., adjust the parameter) when the artificialneuron processed a signal received at its input to generate a processedsignal at its output. In examples, the machine learning algorithm may beconfigured to adjust the processing employed by a plurality ofartificial neurons. In examples, processing circuitry 106 is configuredto train the machine learning algorithm such that when processingcircuitry 106 provides an input vector indicative of a cardiac signal ofpatient 104, the trained machine learning algorithm maps the inputvector to an output vector indicative of a representative glucose levelof patient 104.

Processing circuitry 106 may be configured to train the machine learningalgorithm using the training data set in any manner causing the machinelearning algorithm to converge as the training proceeds. In examples,processing circuitry 106 is configured to use a first portion of thetraining data set to cause the machine learning algorithm to convergeand a second portion of the training data set to validation test and/orblind test the training conducted with the first portion.

Processing circuitry 106 is configured to utilize the trained machinelearning algorithm to provide a representative glucose level of patient104 based on a current cardiac signal received from cardiography system114 (164). For examples, using the trained machine learning algorithm,processing circuitry 106 may receive a cardiac signal of patient 104from cardiography system 114 and formulate an input vector having one ormore elements indicative of the cardiac signal. Processing circuitry 106may be configured to provide the input vector to the trained machinelearning algorithm and utilize the trained machine learning algorithm tomap the input vector to a representative glucose level. Processingcircuitry 106 may be configured to provide the representative glucoselevel as an output to a user interface. The user interface may beconfigured to display the representative glucose level in a formatviewable by patient 104. In examples, processing circuitry may beconfigured to store the representative glucose level in a memoryincluded within medical system 100, and/or communicate therepresentative glucose level to a server and/or one or more othercomputing devices.

In examples, processing circuitry 106 is configured to periodicallyperform a calibration check of the trained machine learning algorithm bydetermining a current glucose level of patient 104 using glucose sensor110 and comparing the current glucose level to a representative glucoselevel determined for patient 104 using the trained machine learningalgorithm. In examples, processing circuitry 106 receives a currentcardiac signal of patient 104 from cardiography system 114 andformulates an input vector having one or more elements indicative of thecurrent cardiac signal. Processing circuitry 106 provide the inputvector to the trained machine learning algorithm and utilizes thetrained machine learning algorithm to map the input vector to arepresentative glucose level. Processing circuitry 106 further receivesa glucose signal indicative of a current glucose level of patient 104from glucose sensor 110. Processing circuitry 106 may be configured tocompare the current glucose level of patient 104 indicated by glucosesensor 110 with the representative glucose level determined by thetrained machine learning algorithm using the current cardiac signal.Based on the comparison, processing circuitry 106 may be configured toformulate additional training data and further train or re-train themachine learning algorithm using the additional training data. Forexample, processing circuitry 106 may be configured to formulateadditional training data by performing one or more of receiving acardiac signal indicative of a cardiac characteristic of heart 102 fromcardiography system 114 (152), activating glucose sensor 110 (154),receiving a glucose signal from glucose sensor 110 (156), deactivatingglucose sensor 110 (158), formulating additional training data (160),and re-training and/or further training the machine learning algorithmusing the additional training data (162).

Processing circuitry 106 may be prompted to perform a calibration checkby any criteria. In examples, processing circuitry is configured toperform a calibration check on a chronological schedule, such asfollowing the elapse of a certain amount of time between calibrationchecks. In examples, processing circuitry 106 is configured to perform acalibration check based on changes in the cardiac signal, such asunexpected changes between specific cardiac markers. For example, if twoor more independent cardiac markers which normally move in concert witha glucose level begin to exhibit movement in opposite and/or unexpecteddirections relative to each other, processing circuitry 106 may beconfigured to perform a calibration check. Processing circuitry 106 maybe configured to perform a calibration check based on changes in aglucose level sensed by glucose sensor 110 relative to changes in one ormore cardiac markers determined from a cardiac signal from cardiaographysystem 114. Processing circuitry 106 may be configured to perform acalibration check under any criteria which may be defined using theglucose signal from glucose sensor 110, the cardiac signal fromcardiography system 114, and/or other physiological parameters ofpatient 104. Processing circuitry 106 may be configured to activateglucose sensor 110 when a calibration check is required, and/or when thecalibration check indicates further training and/or retraining of themachine learning algorithm may be necessary.

Hence, medical system 100 is configured to collect a one or more cardiacsignals indicative of cardiac characteristics of patient 104 fromcardiography system 114 (e.g., sensing circuitry 116) and one or moreglucose signals indicative of glucose levels of patient 104 from glucosesensor 110. Processing circuitry 106 is configured to formulate atraining data set using the cardiac signals and the glucose signalsproduced by patient 104. Processing circuitry 106 is configured to traina machine learning algorithm using the training data set. In examples,the training data set includes a plurality of training input vectorsindicative of the cardiac signals and an associated training outputvector indicative of the glucose signals. Subsequent to the training,medical system 100 is configured to receive a current cardiac signalfrom cardiography system 114 and use the trained machine learningalgorithm to map the current cardiac signal to a representative glucoselevel of patient 104. Medical system may cause glucose sensor 110 todeactivate when the glucose signals are no longer actively required(e.g., once the training data set is formulated, and/or once the machinelearning algorithm is trained). Thus, medical system 100 may beconfigured to substantially reduce and/or minimize the activated time ofan on-board glucose sensor, potentially extending the life of theglucose sensor and decreasing the need for periodic replacement.

Processing circuitry 106 may be configured to deactivate glucose sensor110 to prevent and/or minimize the electrical power received by theelectrode. Deactivating glucose sensor 110 to reduce and/orsubstantially prevent the oxidation of glucose may extend the life ofthe electrode by reducing degradation of the included catalyst, or otherportions of glucose sensor 110. For example, processing circuitry 106may be configured to activate glucose sensor 110 when one or moreglucose signals are required to formulate the training data set and/orperform a calibration check of the trained machine learning algorithm,and may be configured to deactivate glucose sensor 110 when processingcircuitry 106 utilizes the trained machine learning algorithm and acurrent cardiac signal from cardiography system 114 (e.g., sensingcircuitry 116) to determine a representative glucose level.

In examples, housing 108 of medical system 100 mechanically supports oneor more of processing circuitry 106, glucose sensor 110, and/or sensingcircuitry 116. For example, FIG. 3 illustrates an example medical system200 including a housing 208. Medical system 200 is an example of medicalsystem 100. Housing 208 mechanically supports processing circuitry 206and glucose sensor 210. In examples, housing 208 mechanically supportssensing circuitry 216. Housing 208 may be configured as a wearable,implantable, and/or portable device configured to contact a body (e.g.,a torso) of patient 104 (FIG. 1). In some examples, the medical system200 includes one or more electrodes 222 configured to contact patient104 and sense a cardiac characteristic of heart 102. Electrodes 222 maybe configured to communicate the cardiac characteristic to sensingcircuitry 216 via, e.g., a communication link 224. Housing 208 maymechanically support electrodes 222. In some examples, housing 208mechanically supports electrode 222 and/or glucose sensor 210 such thatelectrode 222 and/or glucose sensor 210 may substantially contact thebody of patient 104 when housing 208 contacts the body of patient 104.Housing 208, processing circuitry 206, glucose sensor 210, and sensingcircuitry 216 are examples of housing 108, processing circuitry 106,glucose sensor 210, and sensing circuitry 116 respectively.

Housing 208 may mechanically support a user input device 226 configuredto be actuated by patient 104 and/or another user as needed. User inputdevice 226 may be, for example, a manually operated button on housing108, and/or circuitry configured to receive a communication (e.g., awireless communication) from a smart phone, tablet, another externaldevice, and/or some other device configured for receiving user input. Inexamples, user input device 226 is a multipurpose user interfaceconfigured to initiate multiple operations of medical system 200. Forexample, user input device 226 may be configured to cause one or more ofthe following functions, without limitation: waking up processingcircuitry 206, sensing circuitry 216, and/or other components of medicalsystem 200; triggering sensing circuitry 216 and/or electrode 222 toprovide one or more cardiac signals to processing circuitry 206,activating glucose sensor 210 to cause glucose sensor 210 to provide oneor more glucose signals to processing circuitry 206, deactivatingglucose sensor 210 to cause glucose sensor 210 to cease providingglucose signals to processing circuitry 206, triggering processingcircuitry 206 to formulate a training data set and/or train the machinelearning algorithm, triggering processing circuitry to perform acalibration check of the trained machine learning algorithm, and thelike.

User input device 226 may employ any device to receive an input frompatient 104 and/or another user, including one or more of a button, aslider mechanism, a pin, a lever, a switch, a touch-sensitive element,and the like. User input device 226 may be configured to receive acommunication from a device remote from housing 208 (e.g., a wirelesscommunication) to initiate performance of one or more of theabove-described functions, or other functions. In some examples, medicalsystem 200 includes more than one user input device 226 to initiate thevarious functions described above.

In examples, medical system 200 is a portable device. Medical system 200may be a wearable and/or implantable device configured to be worn bypatient 104. Medical system 200 may include a base surface 228configured to face and/or contact the skin of patient 104 when medicalsystem 200 is worn by patient 104. Housing 208 may be configured tomechanically support glucose sensor 210 and/or electrode 222 such thatat least some portion of glucose sensor 210 and/or electrode 222contacts patient 104 when base surface 228 faces and/or contacts theskin of patient 104. In some examples, medical system 200 includes anadhesive element 230 configured to substantially affix (e.g.,temporarily adhere) the housing 208 to the body of patient 104. Adhesiveelement 230 may be, for example, a piece of double-sided adhesive tapecut into a desired shape and size, and an adhesive liner overlying someportion of base surface 228, or some other type of adhesive element. Insome examples, medical system 200 includes one or more straps and/orother mechanical devices to substantially affix the housing 208 to thebody of patient 104.

As discussed, processing circuitry 106, 206 may be configured toidentify one or more cardiac markers using the cardiac signal receivedfrom sensing circuitry 116, 216. Processing circuitry may be configuredto identify variabilities in the one or more cardiac markers. Inexamples, a cardiac marker identified by processing circuitry 106, 206using the cardiac signal is indicative of changes in autonomic status ofheart 102, such as heart rate variability, acceleration and decelerationcapacity and/or heart rate turbulence. A cardiac marker may beindicative of an arrhythmia of heart 102. In examples, a cardiac markermay be selected to reflect changes in the sympathetic and/orparasympathetic drive of heart 102, such as an increased sympatheticdrive and/or a decreased parasympathetic drive. In some examples, acardiac marker is indicative of a low frequency band of heart ratevariability and/or of an acceleration capacity (e.g., to reflect thesympathetic drive of heart 102). In some examples, a cardiac marker isindicative of a high frequency band of heart rate variability and/or ofa deceleration capacity (e.g., to reflect the parasympathetic drive ofheart 102). A cardiac marker may be indicative of a heart rateturbulence (HRT) of heart 102 (e.g., a turbulence onset (TO) and/or aturbulence slope (TS)) determined using, for example, the heart rateafter premature beats to assess acceleration and deceleration. Inexamples, a cardiac marker is indicative of a deceleration capacity (DC)and/or a deceleration run (DR).

In examples, a cardiac marker identified by processing circuitry 106using the cardiac signal is indicative of a cardiomyopathy. Thecardiomyopathy may be characterized by, for example, a left ventricularhypertrophy, and/or other physiological characteristics of heart 102. Inexamples, a cardiac marker is at least one of a heart rate variability(HRV), a QT internal variability (QTV), a corrected QT interval (QTcand/or QTt), an ST interval, an ST elevation, a T wave amplitude, aT-peak to T-end interval, a T slope, a T-wave area, a T-wave asymmetry,an R-wave amplitude, a T-wave amplitude, an R-wave/T-wave amplitude,and/or another identifiable physiological characteristic of the cardiacsignal of patient 104. A cardiac marker identified may include a T-wavealternans based on two or more T-waves (e.g., by beat-to-beat envelopanalyses). In some examples, a cardiac marker identified by processingcircuitry 106, 206 using the cardiac signal includes a T-wave areavariability including the dimensions of start ending, height slope,and/or symmetry.

A cardiac marker identified by processing circuitry 106, 206 using thecardiac signal may be an HRV time-domain interval measure and/or an HRVfrequency domain measure. For example a cardiac marker may be HRV timedomain interval measure based on a Normal-to-Normal (NN) intervalbetween R peaks of the cardiac signal, such as an SDNN (e.g., standarddeviation of NN intervals), SDANN (e.g., standard deviation of theaverages of NN intervals over a time segment (e.g., 5 mins)), an RMSSD(e.g., square root of the mean of a sum of squares of differencesbetween NN intervals), an NN50 (e.g., number of pairs of adjacent NNintervals differing by more than 50 ms), a pNN50 (e.g., NN50 countdivided by a number of NN intervals), a mNN (e.g., a means of NNintervals), an SDNNindex (e.g., a mean of the standard deviations of NNintervals over a time segment (e.g., 5 mins)), an SD1 (e.g., a pointcareplot of short term variability), an SD22 (e.g., a pointcare plot of longterm variability), and/or other time domain interval measures. A cardiacmarker may be HRV frequency time domain measure reflecting aphysiological parameter of heart 102 in an LF band of from about 0.15 Hzto about 0.04 Hz, a VLF band from about 0.04 Hz to about 0.003 Hz, a ULFband less than about 0.003 Hz, and/or an HF band of from about 0.4 Hz toabout 0.15 Hz.

Processing circuitry 106, 206 may be configured to determine arepresentative glucose level using, for example, the one or more cardiacmarkers, and/or variabilities in the one or more cardiac markers. Inexamples, processing circuitry 106, 206 is configured to identify aplurality of cardiac markers using the cardiac signal and assign aweight to each of the cardiac markers. The machine learning algorithmmay be configured to adjust a weight assigned to a cardiac marker whenprocessing circuitry 106, 206 trains the machine learning algorithm.Processing circuitry 106. 206 and/or the machine learning algorithm maybe configured to adjust and/or correct the one or more cardiac markersbased on characteristics specific to patient 104, such as activitylevel, medication, respiration level, heart rate, age, gender, weight,ST segment changes, time of day (e.g., nocturnal and/or diurnal),comorbidities and/or disease progression, diabetes 1, diabetes 2, and/orother specific characteristics.

As discussed, medical system 100, 200 includes one or more glucosesensors 110, 210 configured to periodically and/or substantiallycontinuously communicate a glucose signal indicative of the glucoselevel of patient 104 to processing circuitry 106. Glucose sensors 110,210 may be configured to generate the glucose signal indicative of theglucose level in any manner, including sensing of electrochemicalpotentials, near-infra-red spectroscopy, impedance spectroscopy, Ramanspectroscopy, tomography, photoacoustics, and other methods.

Processing circuitry 106 may be configured to activate and de-activateglucose sensor 110, 210 in order to, for example, extend an operationallife of glucose sensor 110, 210. For example, glucose sensor 110, 210may be configured to cause an oxidation of glucose using a catalyst suchas a glucose oxidase enzyme. Degradation of the catalyst as a result ofnormal sensing operation of the individual glucose sensor maysubstantially limit the operational life of the individual glucosesensor available. Deactivating glucose sensor 110, 210 to reduce and/orsubstantially prevent the oxidation of glucose may extend the life ofthe electrode by reducing degradation of the included catalyst, or otherportions of glucose sensor 110, 210. Such selective activation anddeactivation may extend the useful life of glucose sensor 110, 210.

In an example, glucose sensor 110, 210 includes one or more electrodesnecessary for the sensing of glucose within the blood and/orinterstitial fluid of patient 104. The one or more electrodes mayinclude a working, reference, and/or counter electrode. The workingelectrode may include an electrochemical sensing stack including ananalyte sensing layer, such as an enzyme layer, for example a glucoseoxidase layer. For example, the enzyme layer may be deposited on theworking electrode. In certain embodiments, the electrochemical sensingstack may include additional layers, such as a protein layer including aprotein such as human serum albumin, bovine serum albumin or the like.The electrochemical sensing stack 50 may further includes an analytemodulating layer, such as a glucose limiting membrane (GLM), over theenzyme layer to regulate analyte contact with the analyte sensing layeror enzyme layer. For example, the analyte modulating membrane layer maybe configured to substantially regulate the amount of glucose thatcontacts an enzyme such as glucose oxidase present in the analytesensing layer.

As an example, FIG. 4 illustrates a cross sectional of an example sensorelectrode 240 which may be included in glucose sensor 110, 210 for thedetection of glucose in blood and/or interstitial fluid of patient 104.Sensor electrode 240 may be formed from a plurality of components thatare typically in the form of layers of various conductive andnon-conductive constituents disposed on each other according to acceptedmethods, although other configurations for sensor electrodes included inglucose sensor 110, 210 may be utilized.

In the example illustrated in FIG. 4, sensor electrode 240 includes abase layer 242 to support one or more portions of sensor electrode 240.Base layer 242 may comprise, for example, a material such as a polymericsubstrate, which may be self-supporting or further supported by anothermaterial. Base layer 242 may be a non-toxic biocompatible polymer, suchas silicone compounds, polyimides, biocompatible solder masks, epoxyacrylate copolymers, or the like. An exemplary base layer 242 ispolyethylene terephthalate (PET), polyimide (PI), or a compositethereof. Sensor electrode 240 may include a conductive layer 244disposed over, and/or directly on and/or combined with, base layer 242.An exemplary conductive layer 244 is platinum. Base layer 242 and/orconductive layers 244 may be generated using many known techniques andmaterials. In certain embodiments, an electrical circuit of glucosesensor 110, 210 is defined by etching the disposed conductive layer 244into a desired pattern of conductive paths. An electrically insulatinglayer may be formed around and/or on some portion of conductive layer244. For example, the electrically insulating layer may be a polymercoating, such as non-toxic biocompatible polymers such as siliconecompounds, polyimides, biocompatible solder masks, epoxy acrylatecopolymers, or the like.

Sensor electrode 240 may be configured such that conductive layer 244 isexposed to an external environment. In examples, an analyte sensinglayer 246 is formed over and/or is disposed on an exposed electrodesurface 248. Sensing layer 246 may be a sensor chemistry layer includingmaterials which undergo a chemical reaction and/or participate in aseries of chemical reactions that generate a signal sensed by conductivelayer 244. Sensing layer 246 may form a sensor surface 250 where ananalyte such as glucose may bind. In some examples, sensing layer 246includes an enzyme 252 which catalyzes some portion of the chemicalreaction and/or the series of chemical reactions. Enzyme 252 may beentrapped within a polymer matrix (e.g., a thermally-cured polymermatrix and/or UV-cured polymer matrix) of sensing layer 246. Enzyme 252may be an material capable of producing and/or utilizing oxygen and/orhydrogen peroxide, such as the enzyme glucose oxidase. In examples,sensor electrode 240 includes a glucose limiting membrane configured tolimit an amount of glucose which reaches sensing layer 246 and/or enzyme252. The glucose limiting membrane may be configured to limit an amountof glucose which reaches sensor surface 250. In examples, the glucoselimiting membrane is configured to limit and/or modulate a glucosesignal provided by a glucose sensor such as glucose sensor 110, 210.

In examples, the enzyme 252 such as glucose oxidase in the sensing layer246 reacts with glucose to produce hydrogen peroxide, a compound whichthen modulates a current at the electrode surface 248. As thismodulation of current depends on the concentration of hydrogen peroxide,and the concentration of hydrogen peroxide correlates to theconcentration of glucose, the concentration of glucose can be determinedby monitoring this modulation in the current. In a specific embodiment,the hydrogen peroxide is oxidized at an electrode surface 248 that is ananode (also termed herein the anodic electrode), with the resultingcurrent being proportional to the hydrogen peroxide concentration. Suchmodulations in the current caused by changing hydrogen peroxideconcentrations can by monitored by any one of a variety of sensordetector apparatuses such as a universal sensor amperometric biosensordetector.

Glucose sensor 110, 210 may be configured to sense a glucose level inthe blood and/or interstitial fluid of patient 104 using electrode 240.Glucose sensor 110, 210 may be configured to sense the glucose levelusing electrode 240 in conjunction with one or more other electrodes,such as a working electrode and/or reference electrode. Glucose sensor110, 210 may be configured to bring the blood and/or interstitial fluidof patient 104 into contact with sensing layer 246 to cause the chemicalreaction and/or series of chemical reactions generating a signal (e.g.,a current at electrode surface 250).

With continued use of glucose sensor 110, 210, the accuracy and/orsensitivity of glucose sensor 110, 210 for the detection of glucosewithin blood and/or interstitial fluid of patient 104 may drift due todegradation caused by exposure to various electroanalytical species suchas oxygen radicals. Further, post-implantation effects such asbiofouling and foreign body response may also contribute to passivationof the electro catalytic activity of one or more of the electrodescomprising glucose sensor 110, 210. Thus, the useful life of glucosesensor 110, 210 may act to limit the lifetime over which medical system100, 200 may be consistently employed. In examples, and as discussed,medical system 100, 200 is configured to extend an operational life ofglucose sensor 110, 210 by activating glucose sensor 110, 210 whenglucose signals are required (e.g., when formulating a training data setand/or performing a calibration check), and deactivating glucose sensor110, 210 when the glucose signals are no longer required (e.g., when thetrained machine learning algorithm is used to determine representativeglucose levels).

In some examples, glucose sensor 110, 210 includes a plurality ofindividual glucose sensors and processing circuitry 106 is configured toselectively activate a first portion (e.g., a first individual glucosesensor) of the individual glucose sensors to generate the glucosesignal. Processing circuitry 106, 206 may be configured to retain asecond portion of the plurality individual glucose sensors in adeactivated state as the first portion generates the glucose signal,such that medical system 100, 200 may receive the glucose signal withoutpotentially degrading the second portion of the plurality. For example,processing circuitry 106, 206 may be configured to activate the firstindividual glucose sensor by exposing the first individual glucosesensor to the interstitial fluid or blood of patient 104 to generate aglucose signal when required (e.g., while formulating training dataand/or conducting a calibration check). Processing circuitry 106, 206may be configured to deactivate the first individual glucose sensor andactivate a second individual glucose sensor in the plurality in theplurality when a replacement criteria for the first individual glucosesensor is met (e.g., when processing circuitry 106, 206 detects adegraded signal from the first individual glucose sensor, following theelapse of a chronological period of use of the first individual glucosesignal, and/or for another reason). Including a plurality of individualglucose sensors may extend an operational life of medical system 100,200 allowing, for example, patient 104 to utilize medical system 100,200 for a longer period of time prior to replacement.

As an example, FIG. 5 is a plan view of a glucose sensor 310 including aplurality of individual glucose sensors 312. Glucose sensor 310 is anexample of glucose sensor 110, 210. Glucose sensor 310 may include abase substrate 318 suitable for use in manufacturing a plurality ofglucose sensor devices. Each individual glucose sensor may include oneor more sensor electrodes such as sensor electrode 240 (FIG. 3). Eachindividual glucose sensor may include one or more conductive plugs inelectrical communication with a conductive layer (e.g., conductive layer244 (FIG. 3)) of the individual glucose sensor, such as conductive plugs320 of first individual glucose sensor 314. In some examples, basesubstrate 318 is a wafer formed from an appropriate material thataccommodates waferscale manufacturing, such as a semiconductor materialsuch as silicon, a glass material, a ceramic material, a sapphirematerial, a polymer material, a plastic material, or a compositematerial. Base substrate 318 may have any suitable dimensions and/orthickness to accommodate the plurality of individual glucose sensors312.

In examples, glucose sensor 310 may be configured such that a firstindividual glucose sensor (e.g., first individual glucose sensor 314)may be exposed to blood and/or interstitial fluid of patient 104 whileother individual glucose sensors remain isolated. For example, eachindividual glucose sensor may be substantially surrounded by a cavity orwell, such as cavity 322 substantially surrounding first glucose sensor310. Cavity 322 may be configured to enclose a volume of fluid (e.g.,blood and/or interstitial fluid of patient 104) and establish fluidcommunication between the volume of fluid and one or more sensorelectrodes of first individual glucose sensor 314, such that firstindividual glucose sensor 314 generates a glucose signal indicative of aglucose level within the volume of fluid. Cavity 322 may be configuredto maintain a fluid isolation between the volume of fluid and otherindividual glucose sensors within the plurality of individual glucosesensors 312, such that the other individual glucose sensors (and anyenzymes, e.g., glucose oxidase) are not exposed to the volume of fluid.Processing circuitry 106, 206 may be configured to activate firstindividual glucose sensor 314 within cavity 322 to generate a glucosesignal until a replacement criteria for first individual glucose sensor314 is met, then activate individual glucose sensor 316 within a cavity324 to provide the glucose signal. Processing circuitry 106, 206 may beconfigured to selectively activate individual glucose sensors in thismanner for substantially all individual glucose sensors within theplurality of glucose sensors 312 in order to, for example, extend aworking life of glucose sensor 110, 210.

FIG. 6 is illustrates a flow diagram of an example technique which maybe utilized by processing circuitry 106, 206 based on a representativeglucose level of patient 104 determined using a cardiac signal of heart102. Processing circuitry 106, 206 may be configured to utilize all orany portion of the technique illustrated by FIG. 6.

Processing circuitry 106, 206 may be configured to determine arepresentative glucose level (402). Processing circuitry 106, 206 mayevaluate the representative glucose level to determine if therepresentative glucose level is below a low threshold (404) and/or todetermine if the representative glucose level is above a high thresholdand/or trending toward a high threshold (406). The low threshold and/orthe high threshold may be generalized levels, or may be specific topatient 104 and/or a subset of patients such as patient 104.

Processing circuitry 106, 206 may be configured to determine anotherrepresentative glucose level if the representative glucose level is notbelow the low threshold (N at 404). If the representative glucose levelis below the low threshold (Y at 404), processing circuitry 106, 206 maybe configured take additional actions. For example, processing circuitry106, 206 may be configured to send a low threshold alarm to patient 104(e.g., using a user interface) and/or a clinician (408). Processingcircuitry 106, 206 may be configured to send the low threshold alarm viaa portable and/or wearable medical device, a smart phone, a tablet,another processor or computing system, or another external device.Processing circuitry 106, 206 may be configured to recommend an actionto be taken by patient 104 (410), such as ingesting orange juice ortaking some other remedial action.

Processing circuitry 106, 206 may be configured to determine anotherrepresentative glucose level if the representative glucose level is notabove the high threshold (N at 406). If the representative glucose levelis above the high threshold (Y at 406), processing circuitry 106, 206may be configured take additional actions. In examples, processingcircuitry 106, 206 is configured to evaluate whether patient 104 is aknown diabetic or pre-diabetic patient (412). If patient 104 is not aknown diabetic or pre-diabetic patient (N at 412), processing circuitry106, 206 may be configured to detect and/or identify patient 104 as adiabetic or pre-diabetic patient (414). Processing circuitry 106, 206may be configured to send an alarm message to patient 104 and/or anotheruser or clinician (416) via, for example, a portable and/or wearablemedical device, a smart phone, a tablet, another processor or computingsystem, or another external device.

If patient 104 is known diabetic or pre-diabetic patient (Y at 412),processing circuitry 106, 206 may be configured to calibrate the machinelearning algorithm (418) by, for example, formulating additionaltraining data and further training or re-training the machine learningalgorithm using the additional training data. Following calibration,processing circuitry 106, 206 may be configured to determine if therepresentative glucose level of patient 104 indicates a very highglucose level (e.g., above 8.3 mmol/l) (420) or a high glucose level(e.g., above 7.2 mmol/l) (422). Processing circuitry 106, 206 may beconfigured to send an alarm message to patient 104 and/or another useror clinician (416) based on determining a very high glucose level (420)or high glucose level (422).

Processing circuitry 106, 206 may be configured suggest one or moretreatment routes based on the representative glucose level determinedfor patient 104. For example, the treatment route suggested may be forpatient 104 to adapt and/or adjust medication (424). The treatment routesuggested may be a recommendation to inject insulin (e.g., manually orautomatically) (426). The treatment route suggested may be to confirmthe results of processing circuitry 106, 206 using additional testing,such as a finger blood test. (428). The treatment route suggested may beto consult a physician (430).

In examples, processing circuitry 106, 206 is configured to evaluateand/or analyze high thresholds and low thresholds indicated by therepresentative glucose level of patient 104. Processing circuitry 106,206 may be configured to evaluate and/or analyze the high thresholds andlow thresholds over a certain time window. The time window may be, forexample, a floating time window (e.g, a time window having a start timeand/or stop time that adjusts as chronological time progresses). Inexamples, processing circuitry 106, 206 is configured to store the valueof the high thresholds and the value of the low thresholds detected. Thevalue of a high threshold and the value of a low threshold may bedetermined using the representative glucose level.

FIG. 7 is a block diagram illustrating an example medical system 500configured to communicate with one or more external systems. Medicalsystem 500 includes processing circuitry 506, communication circuitry558, storage device 560, and a user interface 562. Medical system 500 isan example of medical system 100, 200 and processing circuitry 506 is anexample of processing circuitry 106, 206. Processing circuitry 506 mayinclude one or more processors that are configured to implementfunctionality and/or process instructions for execution within medicalsystem 500. For example, processing circuitry 506 may be capable ofprocessing instructions stored in storage device 560. Processingcircuitry 506 may include, for example, microprocessors, DSPs, ASICs,FPGAs, or equivalent discrete or integrated logic circuitry, or acombination of any of the foregoing devices or circuitry. Accordingly,processing circuitry 506 may include any suitable structure, whether inhardware, software, firmware, or any combination thereof, to perform thefunctions ascribed herein to processing circuitry 506.

Communication circuitry 558 may include any suitable hardware, firmware,software or any combination thereof for communicating within medicalsystem 500 or with another device and/or system external to medicalsystem 500. Communication circuitry 558 may be configured to receiveand/or send communications under the control of processing circuitry506. In examples, communication circuitry 558 is configured to send andreceive communications via communication links 115, 120, 224, and othercommunication links within medical system 500. In examples,communication circuitry 558 is configured to send communications toand/or receive communications, including downlink and uplink telemetry,from devices and/or system external to medical system 500. Communicationcircuitry 558 may be configured to transmit or receive signals viainductive coupling, electromagnetic coupling, Near Field Communication(NFC), Radio Frequency (RF) communication, Bluetooth, Wi-Fi, or otherproprietary or non-proprietary wireless communication schemes.Communication circuitry 558 may be configured to communicate via any ofa variety of forms of wired and/or wireless communication and/or networkprotocols.

Storage device 560 may be configured to store information within medicalsystem 500 during operation. Storage device 560 may include acomputer-readable storage medium or computer-readable storage device. Insome examples, storage device 560 includes one or more of a short-termmemory or a long-term memory. Storage device 560 may include, forexample, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories,or forms of EPROM or EEPROM. Storage device 560 may be used to store atleast a portion of the cardiac signal generated by sensing circuitry116, 216 and/or the glucose signal generated by glucose sensor 110, 210.In examples, storage device 560 is used to store data indicative ofinstructions for execution by processing circuitry 506. Storage device560 may be used by software or applications running within medicalsystem 500 to temporarily store information during program execution.

A user, such as patient 104 or a clinician, may interact with medicalsystem 500 through user interface 562. User interface 562 may include avisual display with which processing circuitry 506 may presentinformation related a representative glucose level determined forpatient 104, cardiac signals received from sensing circuitry 116, 216,glucose signals received from glucose sensor 110, 210, otherphysiological characteristics related to patient 104 such as heart rate,blood pressure, current glucose level (e.g., using glucose sensor 110,210), and others. In addition, user interface 562 may include an inputmechanism configured to receive input from patient 104 and/or aclinician. The input mechanism may include, for example, any one or moreof buttons, a keypad (e.g., an alphanumeric keypad), a peripheralpointing device, a touch screen, or another input mechanism that allowsthe user to navigate through user interfaces presented by processingcircuitry 506 and provide input. In other examples, user interface 562also includes audio circuitry for providing audible notifications,instructions or other sounds to the user, receiving voice commands fromthe user, or both.

Medical system 500 may be configured to configured to couple to anetwork 566 (e.g., via an access point 564) in accordance with one ormore techniques described herein. For example, medical system 500 mayuse communication circuitry 558 to communicate with access point 564 viaa hard-line or wireless connection. In the example of FIG. 7, medicalsystem 500, access point 564, server 568, and/or computing devices570A-570N may be interconnected and may communicate with each otherthrough network 566. Access point 564 may include a device that connectsto network 566 via any of a variety of connections, such as telephonedial-up, digital subscriber line (DSL), or cable modem connections. Inother examples, access point 564 may be coupled to network 566 throughdifferent forms of connections, including wired or wireless connections.

Server 568 may be configured to provide a secure storage site for datathat has been collected by medical system 500. In some cases, server 568may assemble data in web pages or other documents for viewing by trainedprofessionals, such as clinicians, via computing devices 570A-570N. Inexamples, server 568 may comprise one or more servers, a cloud, one ormore databases, and/or a data center. Server 568 may include a storagedevice 572 (e.g., a memory device) to, for example, store data retrievedfrom medical system 500. Server 568 may include processing circuitry 574including one or more processors that are configured to implementfunctionality and/or process instructions for execution within server568. For example, processing circuitry 574 may be capable of processinginstructions stored in storage device 572. Processing circuitry 574 mayinclude, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalentdiscrete or integrated logic circuitry, or a combination of any of theforegoing devices or circuitry. Storage device 572 may include acomputer-readable storage medium or computer-readable storage device. Insome examples, storage device 572 includes one or more of a short-termmemory or a long-term memory, such as RAM, DRAM, SRAM, magnetic discs,optical discs, flash memories, or forms of EPROM or EEPROM.

In examples, processing circuitry 574 includes or is a portion ofprocessing circuitry 506. Processing circuitry 574 may be configured toperform all of some portion of the functionality described with respectto processing circuitry 506. Medical system 500 may be configured toprovide data to server 568 to enable processing circuitry 574 to performany portion of the functionality described with respect to processingcircuitry 506.

A technique for determining a representative glucose level of a patientis illustrated in FIG. 8. Although the technique is described mainlywith reference to medical system 100, 200, 500 of FIGS. 1-7, thetechnique may be applied to other medical systems in other examples.

The technique includes formulating, using processing circuitry 106, 206,506, a training data set including one or more training input vectorsand one or more training output vectors (602). The technique may includereceiving, using processing circuitry 106, 206, 506, a cardiac signalindicative of an cardiac characteristic of a heart 102 of a patient 104from sensing circuitry 116, 216. The technique may include receiving,using processing circuitry 106, 206, 506, a glucose signal indicate of aglucose level of patient 104 using glucose sensor 110, 210 310.Processing circuitry 106, 206, 506 may be configured to formulate atraining input vector including one or more elements representative ofthe received cardiac signal and a corresponding training output vectorincluding one or more elements of the representative of the receivedglucose signal.

The technique may include grouping, using processing circuitry 106, 206,506, a given training input vector and the associated training outputvector into a data pair. In examples, processing circuitry 106, 206, 506is configured to formulate a plurality of training input vectorsindicative of a cardiac signal, a cardiac segment, and/or a cardiacmarker received from sensing circuitry 116, 216 and associate a trainingoutput vector indicative of a glucose signal received from glucosesensor 110, 210, 310 for each training input vector. Processingcircuitry 106, 206, 506 may group each training input vector andassociated training output vector in a data pair, such that processingcircuitry 106, 206, 506 formulates a plurality of data pairs.

The technique includes training, using processing circuitry 106, 206,506, a machine learning algorithm using the one or more training datasets (604). Processing circuitry 106, 206, 506 may train the machinelearning algorithm using one or more neural network systems, deeplearning systems, or other types of supervised or unsupervised machinelearning systems. The technique may include processing circuitry 106,206, 506 training the machine learning algorithm using the training dataset formulated. In examples, processing circuitry 106, 206, 506 providesone or more elements of a training input vector of the formulatedtraining data set to the inputs of the machine learning algorithm (e.g.,to one or more inputs of one or more input artificial neurons in anartificial neural network). The technique may include using the machinelearning algorithm to provide an resulting output vector (e.g., anoutput vector at the outputs of one or more output artificial neurons inthe artificial neural network) subsequent to processing circuitry 106,206, 506 providing the training input vector.

The technique may include comparing, using processing circuitry 106,206, 506, the training output vector associated with the training inputvector with the resulting output vector to determine an error, andadjust the processing employed by the machine learning algorithm basedon the error. The technique may include training, using processingcircuitry 106, 206, 506, the machine learning algorithm such that whenprocessing circuitry 106, 206, 506 provides an input vector indicativeof a cardiac signal of patient 104, the trained machine learningalgorithm maps the input vector to an output vector indicative of arepresentative glucose level of patient 104. In examples, processingcircuitry 106, 206, 506 is configured to use a first portion of thetraining data set to cause the machine learning algorithm to convergeand a second portion of the training data set to validation test and/orblind test the training conducted with the first portion.

The technique includes determining, using processing circuitry 106, 206,506, a representative glucose level of patient 104 using the trainedmachine learning algorithm (606). The technique may include determiningthe representative glucose level of patient 104 based on a currentcardiac signal received from sensing circuitry 116, 216. In examples,the technique includes receiving a current cardiac signal of patient 104from sensing circuitry 116, 216 using processing circuitry 106, 206, 506and formulating, using processing circuitry 106, 206, 506, an inputvector having one or more elements indicative of the current cardiacsignal. Processing circuitry 106, 206, 506 may provide the input vectorto the trained machine learning algorithm and utilize the trainedmachine learning algorithm to map the input vector to a representativeglucose level. In examples, processing circuitry 106, 206, 506 providesthe representative glucose level as an output to user interface 562. Inexamples, processing circuitry 106, 206, 506 stores the representativeglucose level in storage device 560 and/or communicates therepresentative glucose level to a device and/or system external tomedical system 100, 200, such as server 568.

The technique may include performing, using processing circuitry 106,206, 506, a calibration check of the trained machine learning algorithm.Processing circuitry 106, 206, 506 may determine a current glucose levelof patient 104 using glucose sensor 110, 210, 310 and compare thecurrent glucose level to a representative glucose level determined forpatient 104 using the trained machine learning algorithm. Processingcircuitry 106 may receive a current cardiac signal of patient 104 fromsensing circuitry 116, 216 and generate a representative glucose levelusing the current cardiac signal and the trained machine learningalgorithm. Processing circuitry 106, 206, 506 may compare the currentglucose level of patient 104 indicated by glucose sensor 110, 210, 310with the representative glucose level determined by the trained machinelearning algorithm. Based on the comparison, processing circuitry 106may formulate additional training data and further train or re-train themachine learning algorithm using the additional training data. Forexample, processing circuitry 106, 206, 506 may be configured toformulate additional training data by performing one or more offormulating the training data set (602) and training the machinelearning algorithm using the training data set (604).

The present disclosure includes the following examples.

Example 1: A medical system comprising: a glucose sensor configured todetermine a glucose level in a patient; and processing circuitryoperably coupled to the glucose sensor, the processing circuitryconfigured to: receive a glucose signal indicative of the glucose levelfrom the glucose sensor, receive a cardiac signal indicative of acardiac characteristic of the patient, associate the glucose signal withthe cardiac signal, formulate one or more training data sets including atraining input vector and a training output vector, wherein the traininginput vector is representative of the cardiac signal and the trainingoutput vector is representative of the glucose signal associated withthe cardiac signal, train a machine learning algorithm using the one ormore training data sets, and determine a representative glucose levelusing the trained machine learning algorithm and a current cardiacsignal, wherein the current cardiac signal is indicative of a currentcardiac characteristic of the heart of the patient.

Example 2: The medical system of example 1, further comprising sensingcircuitry operably connected to the processing circuitry, wherein thesensing circuitry is configured to sense the cardiac characteristic andgenerate the cardiac signal based on the sensed cardiac characteristic,and wherein the processing circuitry is configured to receive thecardiac signal from the sensing circuitry.

Example 3: The medical system of example 1 or 2, further comprising aportable device configured to be worn by the patient, wherein portabledevice includes a housing configured to support at least the glucosesensor and the processing circuitry.

Example 4: The medical system of any of examples 1-3, further comprisinga plurality of individual glucose sensors, wherein the glucose sensor isone of the individual glucose sensors in the plurality, and wherein theprocessing circuitry is configured to select the one of the individualglucose sensors.

Example 5: The medical system of any of examples 1-4, the glucose sensoris configured to provide the glucose signal to the processing circuitryin an activated configuration and not provide the glucose signal to theprocessing circuitry in a deactivated configuration, the glucose sensoris configured to generate the glucose signal using an enzyme, and theprocessing circuitry is configured to cause the glucose sensor toestablish the activated configuration or the deactivated configuration.

Example 6: The medical system of example 5, wherein the glucose sensorincludes one or more electrodes configured to detect a reaction productgenerated from a catalysis of glucose using the enzyme.

Example 7: The medical system of example 5 or 6, wherein the processingcircuitry is configured to cause the glucose sensor to establish thedeactivated configuration when the processing circuitry determines therepresentative glucose level using the trained machine learningalgorithm and the current cardiac signal.

Example 8: The medical system of any of examples 1-7, wherein theglucose sensor is configured to determine the glucose level in thepatient using at least one of near-infra-red spectroscopy, impedancespectroscopy, Raman spectroscopy, tomography, or photoacoustics.

Example 9: The medical system of any of examples 1-8, wherein theglucose sensor is configured to determine the glucose level in thepatient using at least one of monitoring an optical property of afluorescent enzyme, monitoring an optical property of a labeled enzymes,monitoring an optical property of a co-enzyme, monitoring an opticalproperty of a co-substrate, measuring a product of enzymatic oxidationof glucose by glucose oxidase, using synthetic boronic acids, usingConcanavalin A, or applying glucose-binding proteins.

Example 10: The medical system of any of examples 1-9, wherein theprocessing circuitry is configured to perform a calibration check by:receiving a current glucose signal from the glucose sensor, wherein thecurrent glucose signal is indicative of a current glucose level in thepatient, receiving the current cardiac signal, and comparing therepresentative glucose level and the current glucose level indicated bythe current glucose signal.

Example 11: The medical system of any of examples 1-10, wherein theprocessing circuitry is configured to perform a calibration check basedon one or more of an elapse of a chronological time period, a comparisonof a representative glucose level and a current glucose level of thepatient, an evaluation of changes in a cardiac marker determined usingthe cardiac signal, an evaluation of relative changes in two or morecardiac markers determined using the cardiac marker, an evaluation ofchanges in a glucose signal relative to the cardiac marker determinedusing the cardiac signal, or an evaluation of a physiological parameterof the patient.

Example 12: The medical system of any of examples 1-11, wherein theprocessing circuitry is configured to: perform a calibration check ofthe medical system, formulate one or more additional training data setsbased on an evaluation of the calibration check, and conduct additionaltraining of the trained machine learning algorithm using the one or moreadditional training data sets.

Example 13: The medical system of any of examples 1-12, furthercomprising a user input device operably coupled to the processingcircuitry and configured to cause the processing circuitry to receivethe cardiac signal, receive the glucose signal, associate the cardiacsignal with the glucose level, formulate the one or more training datasets, train the machine learning algorithm, and determine therepresentative glucose level.

Example 14: The medical system of any of examples 1-13, wherein theprocessing circuitry is configured to divide the cardiac signal into asegment, wherein the segment is a portion of the cardiac signal receivedover a time interval, and wherein the glucose signal associated with thecardiac signal is portion of the glucose signal associated with the timeinterval.

Example 15: The medical system of any of examples 1-14, wherein themachine learning algorithm is an artificial neural network including oneor more input artificial neurons, and wherein the processing circuitryis configured to train the machine learning algorithm by providing thetraining input vector to the one or more input artificial neurons.

Example 16: The medical system of example 15, wherein: the processingcircuitry is configured to provide the training input vector to themachine learning algorithm, the machine learning algorithm is configuredto generate a resulting output vector when the processing circuitryprovides the training input vector, and the machine learning algorithmis configured to adjust a parameter of one or more artificial neuronswithin the machine learning algorithm based on a comparison of theresulting output vector and the training output vector.

Example 17: The medical system of any of examples 1-16, wherein: theprocessing circuitry is configured to identify a cardiac marker usingthe cardiac signal and associate the cardiac marker with the glucoselevel, the training input vector is representative of the cardiacmarker, and the processing circuitry is configured to determine therepresentative glucose level using the trained machine learningalgorithm and the cardiac marker.

Example 18: The medical system of any of examples 1-17, wherein: theprocessing circuitry is configured to identify a cardiac marker usingthe cardiac signal and associate the cardiac marker with the glucoselevel, and the cardiac marker is at least one of a heart ratevariability (HRV), a QT internal variability (QTV), a corrected QTinterval (QTc and/or QTt), an ST interval, an ST elevation, a T waveamplitude, a T-peak to T-end interval, a T slope, a T-wave area, aT-wave asymmetry, an R-wave amplitude, a T-wave amplitude, anR-wave/T-wave amplitude, a T-wave alternans, a T-wave area variability,a heart rate variability in the time and frequency domain, a heart rateturbulence onset (TO), a turbulence slope (TS), a deceleration capacity(DC), or a deceleration run (DR).

Example 19: The medical system of any of examples 1-18, wherein: theprocessing circuitry is configured to identify a plurality of cardiacmarkers using the cardiac signal and assign a weight to each of thecardiac markers, and the processing circuitry is configured to adjustthe assigned weights when the processing circuitry trains the machinelearning algorithm.

Example 20: The medical system of any of examples 1-19, wherein: theprocessing circuitry is configured to identify a cardiac marker usingthe cardiac signal and associate the cardiac marker with the glucoselevel, and the processing circuitry is configured to correct the cardiacmarker based on an activity level of the patient, a medication taken bythe patient, a respiration level of the patient, a heart rate of thepatient, an age of the patient, a gender of the patient, a weight of thepatient, a change in an ST segment of the patient, a time of day, acomorbidity of the patient, a disease of the patient, diabetes 1 of thepatient, or diabetes 2 of the patient

Example 21: A medical system comprising: a glucose sensor configured todetermine a glucose level in a patient; sensing circuitry configured tosense a cardiac characteristic of a heart of the patient; processingcircuitry operably coupled to the glucose sensor and the sensingcircuitry, the processing circuitry configured to: receive an cardiacsignal indicative of the cardiac characteristic from the sensingcircuitry, identify a cardiac marker using the cardiac signal, receive aglucose signal indicative of the glucose level from the glucose sensor,associate the cardiac marker with the glucose signal, formulate one ormore training data sets including a training input vector and a trainingoutput vector, wherein the training input vector is representative ofthe cardiac signal and the training output vector is representative ofthe glucose signal associated with the glucose signal, train a machinelearning algorithm using the one or more training data sets, anddetermine a representative glucose level using the trained machinelearning algorithm and a current cardiac signal received from thesensing circuitry, wherein the current cardiac signal is indicative of acurrent cardiac characteristic of the heart of the patient; and ahousing mechanically supporting the glucose sensor and the processingcircuitry.

Example 22: The medical system of example 21, wherein: the glucosesensor is configured to provide the glucose signal to the processingcircuitry in an activated configuration and not provide the glucosesignal to the processing circuitry in a deactivated configuration, theglucose sensor is configured to generate the glucose signal using anenzyme, and the processing circuitry is configured to cause the glucosesensor to establish the activated configuration or the deactivatedconfiguration.

Example 23: The medical system of example 21 or 22, wherein theprocessing circuitry is configured to perform a calibration check by:receiving a current glucose signal from the glucose sensor, wherein thecurrent glucose signal is indicative of a current glucose level in thepatient, receiving the current cardiac signal, comparing therepresentative glucose level and the current glucose level indicated bythe current glucose signal, formulating one or more additional trainingdata sets based on the comparison of the representative glucose leveland the current glucose level indicated by the current glucose signal,and conducting additional training of the trained machine learningalgorithm using the one or more additional training data sets.

Example 24: A method comprising: receiving, using processing circuitry,an ECG signal indicative of an electrocardiogram of a heart of a patientfrom sensing circuitry configured to sense the electrocardiogram,receiving, using the processing circuitry, a glucose signal indicativeof a glucose level of the patient from a glucose sensor configured todetermine the glucose level in the patient, associating, using theprocessing circuitry, the ECG signal with the glucose level,formulating, using the processing circuitry, one or more training datasets including a training input vector and a training output vector,wherein the training input vector is representative of the ECG signaland the training output vector is representative of the glucose signalassociated with the ECG signal, training, using the processingcircuitry, a machine learning algorithm using the one or more trainingdata sets, and determining, using the processing circuitry, arepresentative glucose level using the trained machine learningalgorithm and a current cardiac signal, wherein the current cardiacsignal is indicative of a current cardiac characteristic of the heart ofthe patient.

Example 25: The method of example 24, further comprising: establishing,using the processing circuitry, the glucose sensor in an activatedconfiguration, wherein the glucose sensor is configured to generate theglucose signal in the activated configuration, receiving the glucosesignal with the glucose sensor in the activated configuration,establishing, using the processing circuitry, the glucose sensor in adeactivated configuration, wherein the glucose sensor is configured tonot generate the glucose signal in the deactivated configuration, anddetermining the representative glucose level with the glucose sensor inthe deactivated configuration.

Various examples of the disclosure have been described. Any combinationof the described systems, operations, or functions is contemplated.These and other examples are within the scope of the following claims.

What is claimed is:
 1. A medical system comprising: a glucose sensorconfigured to determine a glucose level in a patient; and processingcircuitry operably coupled to the glucose sensor, the processingcircuitry configured to: receive a glucose signal indicative of theglucose level from the glucose sensor, receive a cardiac signalindicative of a cardiac characteristic of the patient, associate theglucose signal with the cardiac signal, formulate one or more trainingdata sets including a training input vector and a training outputvector, wherein the training input vector is representative of thecardiac signal and the training output vector is representative of theglucose signal associated with the cardiac signal, train a machinelearning algorithm using the one or more training data sets, anddetermine a representative glucose level using the trained machinelearning algorithm and a current cardiac signal, wherein the currentcardiac signal is indicative of a current cardiac characteristic of theheart of the patient.
 2. The medical system of claim 1, furthercomprising sensing circuitry operably connected to the processingcircuitry, wherein the sensing circuitry is configured to sense thecardiac characteristic and generate the cardiac signal based on thesensed cardiac characteristic, and wherein the processing circuitry isconfigured to receive the cardiac signal from the sensing circuitry. 3.The medical system of claim 1, further comprising a portable deviceconfigured to be worn by the patient, wherein portable device includes ahousing configured to support at least the glucose sensor and theprocessing circuitry.
 4. The medical system of claim 1, furthercomprising a plurality of individual glucose sensors, wherein theglucose sensor is one of the individual glucose sensors in theplurality, and wherein the processing circuitry is configured to selectthe one of the individual glucose sensors.
 5. The medical system ofclaim 1, wherein: the glucose sensor is configured to provide theglucose signal to the processing circuitry in an activated configurationand not provide the glucose signal to the processing circuitry in adeactivated configuration, the glucose sensor is configured to generatethe glucose signal using an enzyme, and the processing circuitry isconfigured to cause the glucose sensor to establish the activatedconfiguration or the deactivated configuration.
 6. The medical system ofclaim 5, wherein the glucose sensor includes one or more electrodesconfigured to detect a reaction product generated from a catalysis ofglucose using the enzyme.
 7. The medical system of claim 5, wherein theprocessing circuitry is configured to cause the glucose sensor toestablish the deactivated configuration when the processing circuitrydetermines the representative glucose level using the trained machinelearning algorithm and the current cardiac signal.
 8. The medical systemof claim 1, wherein the glucose sensor is configured to determine theglucose level in the patient using at least one of near-infra-redspectroscopy, impedance spectroscopy, Raman spectroscopy, tomography, orphotoacoustics.
 9. The medical system of claim 1, wherein the glucosesensor is configured to determine the glucose level in the patient usingat least one of monitoring an optical property of a fluorescent enzyme,monitoring an optical property of a labeled enzymes, monitoring anoptical property of a co-enzyme, monitoring an optical property of aco-substrate, measuring a product of enzymatic oxidation of glucose byglucose oxidase, using synthetic boronic acids, using Concanavalin A, orapplying glucose-binding proteins.
 10. The medical system of claim 1,wherein the processing circuitry is configured to perform a calibrationcheck by: receiving a current glucose signal from the glucose sensor,wherein the current glucose signal is indicative of a current glucoselevel in the patient, receiving the current cardiac signal, andcomparing the representative glucose level and the current glucose levelindicated by the current glucose signal.
 11. The medical system of claim1, wherein the processing circuitry is configured to perform acalibration check based on one or more of an elapse of a chronologicaltime period, a comparison of a representative glucose level and acurrent glucose level of the patient, an evaluation of changes in acardiac marker determined using the cardiac signal, an evaluation ofrelative changes in two or more cardiac markers determined using thecardiac marker, an evaluation of changes in a glucose signal relative tothe cardiac marker determined using the cardiac signal, or an evaluationof a physiological parameter of the patient.
 12. The medical system ofclaim 1, wherein the processing circuitry is configured to: perform acalibration check of the medical system, formulate one or moreadditional training data sets based on an evaluation of the calibrationcheck, and conduct additional training of the trained machine learningalgorithm using the one or more additional training data sets.
 13. Themedical system of claim 1, further comprising a user input deviceoperably coupled to the processing circuitry and configured to cause theprocessing circuitry to receive the cardiac signal, receive the glucosesignal, associate the cardiac signal with the glucose level, formulatethe one or more training data sets, train the machine learningalgorithm, and determine the representative glucose level.
 14. Themedical system of claim 1, wherein the processing circuitry isconfigured to divide the cardiac signal into a segment, wherein thesegment is a portion of the cardiac signal received over a timeinterval, and wherein the glucose signal associated with the cardiacsignal is portion of the glucose signal associated with the timeinterval.
 15. The medical system of claim 1, wherein the machinelearning algorithm is an artificial neural network including one or moreinput artificial neurons, and wherein the processing circuitry isconfigured to train the machine learning algorithm by providing thetraining input vector to the one or more input artificial neurons. 16.The medical system of claim 15, wherein: the processing circuitry isconfigured to provide the training input vector to the machine learningalgorithm, the machine learning algorithm is configured to generate aresulting output vector when the processing circuitry provides thetraining input vector, and the machine learning algorithm is configuredto adjust a parameter of one or more artificial neurons within themachine learning algorithm based on a comparison of the resulting outputvector and the training output vector.
 17. The medical system of claim1, wherein: the processing circuitry is configured to identify a cardiacmarker using the cardiac signal and associate the cardiac marker withthe glucose level, the training input vector is representative of thecardiac marker, and the processing circuitry is configured to determinethe representative glucose level using the trained machine learningalgorithm and the cardiac marker.
 18. The medical system of claim 1,wherein: the processing circuitry is configured to identify a cardiacmarker using the cardiac signal and associate the cardiac marker withthe glucose level, and the cardiac marker is at least one of a heartrate variability (HRV), a QT internal variability (QTV), a corrected QTinterval (QTc and/or QTt), an ST interval, an ST elevation, a T waveamplitude, a T-peak to T-end interval, a T slope, a T-wave area, aT-wave asymmetry, an R-wave amplitude, a T-wave amplitude, anR-wave/T-wave amplitude, a T-wave alternans, a T-wave area variability,a heart rate variability in the time and frequency domain, a heart rateturbulence onset (TO), a turbulence slope (TS), a deceleration capacity(DC), or a deceleration run (DR).
 19. The medical system of claim 1,wherein: the processing circuitry is configured to identify a pluralityof cardiac markers using the cardiac signal and assign a weight to eachof the cardiac markers, and the processing circuitry is configured toadjust the assigned weights when the processing circuitry trains themachine learning algorithm.
 20. The medical system of claim 1, wherein:the processing circuitry is configured to identify a cardiac markerusing the cardiac signal and associate the cardiac marker with theglucose level, and the processing circuitry is configured to correct thecardiac marker based on an activity level of the patient, a medicationtaken by the patient, a respiration level of the patient, a heart rateof the patient, an age of the patient, a gender of the patient, a weightof the patient, a change in an ST segment of the patient, a time of day,a comorbidity of the patient, a disease of the patient, diabetes 1 ofthe patient, or diabetes 2 of the patient.
 21. A medical systemcomprising: a glucose sensor configured to determine a glucose level ina patient; sensing circuitry configured to sense a cardiaccharacteristic of a heart of the patient; processing circuitry operablycoupled to the glucose sensor and the sensing circuitry, the processingcircuitry configured to: receive an cardiac signal indicative of thecardiac characteristic from the sensing circuitry, identify a cardiacmarker using the cardiac signal, receive a glucose signal indicative ofthe glucose level from the glucose sensor, associate the cardiac markerwith the glucose signal, formulate one or more training data setsincluding a training input vector and a training output vector, whereinthe training input vector is representative of the cardiac signal andthe training output vector is representative of the glucose signalassociated with the glucose signal, train a machine learning algorithmusing the one or more training data sets, and determine a representativeglucose level using the trained machine learning algorithm and a currentcardiac signal received from the sensing circuitry, wherein the currentcardiac signal is indicative of a current cardiac characteristic of theheart of the patient; and a housing mechanically supporting the glucosesensor and the processing circuitry.
 22. The medical system of claim 21,wherein: the glucose sensor is configured to provide the glucose signalto the processing circuitry in an activated configuration and notprovide the glucose signal to the processing circuitry in a deactivatedconfiguration, the glucose sensor is configured to generate the glucosesignal using an enzyme, and the processing circuitry is configured tocause the glucose sensor to establish the activated configuration or thedeactivated configuration.
 23. The medical system of claim 21, whereinthe processing circuitry is configured to perform a calibration checkby: receiving a current glucose signal from the glucose sensor, whereinthe current glucose signal is indicative of a current glucose level inthe patient, receiving the current cardiac signal, comparing therepresentative glucose level and the current glucose level indicated bythe current glucose signal, formulating one or more additional trainingdata sets based on the comparison of the representative glucose leveland the current glucose level indicated by the current glucose signal,and conducting additional training of the trained machine learningalgorithm using the one or more additional training data sets.
 24. Amethod comprising: receiving, using processing circuitry, an ECG signalindicative of an electrocardiogram of a heart of a patient from sensingcircuitry configured to sense the electrocardiogram, receiving, usingthe processing circuitry, a glucose signal indicative of a glucose levelof the patient from a glucose sensor configured to determine the glucoselevel in the patient, associating, using the processing circuitry, theECG signal with the glucose level, formulating, using the processingcircuitry, one or more training data sets including a training inputvector and a training output vector, wherein the training input vectoris representative of the ECG signal and the training output vector isrepresentative of the glucose signal associated with the ECG signal,training, using the processing circuitry, a machine learning algorithmusing the one or more training data sets, and determining, using theprocessing circuitry, a representative glucose level using the trainedmachine learning algorithm and a current cardiac signal, wherein thecurrent cardiac signal is indicative of a current cardiac characteristicof the heart of the patient.
 25. The method of claim 24, furthercomprising: establishing, using the processing circuitry, the glucosesensor in an activated configuration, wherein the glucose sensor isconfigured to generate the glucose signal in the activatedconfiguration, receiving the glucose signal with the glucose sensor inthe activated configuration, establishing, using the processingcircuitry, the glucose sensor in a deactivated configuration, whereinthe glucose sensor is configured to not generate the glucose signal inthe deactivated configuration, and determining the representativeglucose level with the glucose sensor in the deactivated configuration.